DEVELOPMENT... OpenML
Run
160193

Run 160193

Task 3841 (Supervised Classification) mfeat-factors Uploaded 13-02-2015 by Felicia West
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Flow

weka.LogitBoost_DecisionStump(2)J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
weka.LogitBoost_DecisionStump(2)_E1
weka.LogitBoost_DecisionStump(2)_H1.0
weka.LogitBoost_DecisionStump(2)_I80
weka.LogitBoost_DecisionStump(2)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(2)_O1
weka.LogitBoost_DecisionStump(2)_P100
weka.LogitBoost_DecisionStump(2)_S1
weka.LogitBoost_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.LogitBoost_DecisionStump(2)_Z3.0

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

model
Model readable

A human-readable description of the model that was built.

model
Model serialized

A serialized description of the model that can be read by the tool that generated it.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

22 Evaluation measures

Area under ROC curve
0.9945
Per class
F measure
0.9975
Per class
Kappa
0.986
KB relative information score
1956.7259
Mean absolute error
0.003
Mean prior absolute error
0.1803
Number of instances
2000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9975
Per class
Predictive accuracy
0.9975
Prior entropy
0.4703
Recall
0.9975
Per class
Relative absolute error
0.0165
Root mean prior squared error
0.3
Root mean squared error
0.0486
Root relative squared error
0.1621
SciMark benchmark
924.6116
UserCPU time millis
8020
UserCPU time millis training
8020